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Age reading of sardine scale using neural network

使用神经网络读取沙丁鱼鳞的年龄

基本信息

批准号：
03556027
负责人：
AOKI Ichiro
金额：
$ 4.42万
依托单位：
Ocean Research Institute, University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Developmental Scientific Research (B)
财政年份：
1991
资助国家：
日本
起止时间：
1991 至 1993
项目状态：
已结题

项目摘要

1. A personal-computer-based system was developed for counting annual circuli in scales of Japanese sardine using artificial neural network. The image processing was used to digitize the video image of a scale and store the light intensity from five radial transects. The values and positions of the peaks of the intensity along the transect were determined and normalized to input to the neural network as 5^<**>40 mesh two-dimensional data.2. Performance of age determination of the system was examined using a data set from 20 samples of sardine scales. In the learning, the neural network showed learning ability to determine age from training samples without error. However, the performance of the trained neural network for test samples got no further than 50% of correct recognition rate, while error rate was low.3. The followings remain to be examined : (1) much more learning is needed as the number of learning data set were too small, (2) there is room for improvement of illumination to get better image with clear annual circuli, (3) since radial grooves, which have higher contrast than annual circuli in the scale image prevent counting annual circuli correctly, some pre-processing procedure of the image is required to eliminate the noisy grooves, and (4) input data to the neural network should be examined further for larger number of measureing transect and/or larger mesh size than 5^<**>40, which increase resolution power on the one side and the number of units in the neural network on the other hand, i.e. much more calculating time.

1.开发了基于人工神经网络的日本沙丁鱼年环数计算系统。图像处理用于将秤的视频图像数字化，并存储来自五个径向横断面的光强度。确定横断面上强度峰值的大小和位置，并将其归一化，作为5^&lt；**&gt；40目二维数据输入神经网络。使用20个沙丁鱼鳞片样本的数据集检验了该系统的年龄确定性能。在学习中，神经网络表现出了从训练样本中无误差地确定年龄的学习能力。然而，训练好的神经网络对测试样本的识别性能不会超过50%的正确识别率，而错误率较低。(1)由于学习数据集的数量太少，需要更多的学习；(2)为了获得更好的图像，具有清晰的年轮，光照还有改进的余地；(3)由于尺度图像中的径向凹槽比年轮具有更高的对比度，阻碍了对年轮的正确计数，需要对图像进行一些预处理，以消除噪声；(4)对于测量断面数量较多和/或网格尺寸大于5^&lt；**&gt；**&gt；40，这一方面增加了神经网络的分辨率，另一方面增加了神经网络的单元数，即计算时间大大增加。